Projection of temperature and precipitation in Hong Kong in the 21st - PowerPoint PPT Presentation

Projection of temperature and precipitation in Hong Kong in the 21st century using statistical downscaling T C Lee T C Lee Hong Kong Observatory

Global climate projections Global Climate Models / Human factors General Circulation Models (GCMs) (Greenhouse gases, aerosols, etc.) Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Future climate • climate model experiment – 23 models • climate model experiment – 23 models • multi-model data set • 6 greenhouse gas emission scenarios used by IPCC AR4 in global climate simulations • from low to high greenhouse gas emissions are B1, A1T, B2, A1B, A2 and A1FI Low High

Projections of carbon dioxide emission under the six emission scenarios (SRES scenarios) (Source from IPCC) The inset reflects the various assumptions made on the future: 1. Population; 2. Economy; 4. Environment; 5. Equity; 6. Globalisation; and 7. Climate. (Source: IPCC)

Why we need to use downscaling ? Global climate models • Relatively low spatial resolution (150 – 400 km) • May not accurately represent local or station level climate (complex topography, coastal or island locations, etc.) Downscaling is a way to obtain higher spatial resolution output based on GCMs Downscaling technique

Statistical downscaling vs Dynamical downscaling Statistical downscaling Dynamical downscaling Nesting of a finer-scale regional The application of statistical relationships identified in the observed climate model within the coarser global climate model climate, between large and local scale, to GCM outputs Strength • High resolution outputs (10km) Strength • Station level resolution • Station level resolution • Physically consistent with GCM • Physically consistent with GCM • Computational inexpensive • Transferable between regions Weakness • Computationally very demanding • May not be able to transfer between Weakness • Require observational data to regions • Boundary conditions and sub-scale calibrate the downscaling model • Assume stationary relationship processes may affect results between predicand and predictors • Choice of statistical model and predictors may affect results

Statistical downscaling - basic concept Statistical downscaling – develop quantitative relationships between large scale predictors and the local predicand Large scale climate GCM large scale observations outputs (predictors) (predictors) Set up statistical Downscaling model relationships (e.g. regression) Local scale climate Downscaled outputs observation (predicand) (predictand)

Previous work of the Hong Kong Observatory (HKO) Temperature projections • First attempt in 2004 based on IPCC Third Assessment Report model results • Update in 2007 based on IPCC AR4 model results (Leung et al . 2007) 2007) Rainfall projections • First attempt in 2005 based on IPCC Third Assessment Report model results • Update in 2009 based on IPCC AR4 model results (Lee et al . 2009)

Data and methodology Data for setting up downscaling model (monthly mean data) - NCEP re-analysis data / Station observations in southern China - Hong Kong Observatory Headquarters (HKO Hq) observations Model data (acquired from IPCC Data Distribution Centre) - IPCC model monthly mean data - 16 models and 3 emission scenarios (A1B, B1 and A2) Statistical downscaling approach - Empirical linear regression relationship between the climate of southern China and that of the HKO (e.g. Average temperature of southern china <>HKO average temperature) - Urbanization effect was also taken into account for the temperature projection conducted in 2007. (to simulate the combined effect)

Schematic diagram showing the downscaling technique for future temperature in Hong Kong Average temperature of GCM temperature southern China projection over (NCEP reanalysis data) southern China (predictor) Statistical Downscaling model relationships relationships (regression equation) (linear regression) Future urbanization De-urbanized data scenarios HKO Hq (a) frozen urbanization (b) continued urbanization HKO Hq Projected monthly temperatures mean temperatures

Past and projected annual mean temperature anomaly for Hong Kong (relative to the average of 1980-99) Low-end - low GHG emission scenario and frozen urbanization High-end - high GHG emission scenario and continued urbanization Middle-of-the-road - average of the GHG emission scenarios as well as of the two situations regarding urbanization • Average temperature will continue to increase • More very hot days and less cold days

Schematic diagram showing the downscaling technique for future rainfall in Hong Kong Average rainfall of GCM rainfall southern China projections over (Station rainfall) southern China (predictor) Statistical Statistical Downscaling model relationships (regression equation) (linear regression) HKO Hq rainfall Projected monthly observation rainfall

About 11% increase relative to the 1980-1999 average of 2324 mm About 5% or 120mm less than the 1980-1999 average • Annual rainfall +11% by the 2090-2099 decade • -ve decadal rainfall anomaly between 2010 & 2039 • Large variability in decadal rainfall anomaly

Projected occurrence of extremes (rainfall) Increase in extremely dry years from 2 in 20 th century to 4 in 21 st century Increase in extremely wet years from 2 in 20 th century to 10 in 21 st century

Future Work Projection of temperature and rainfall extremes using a high temporal resolution global climate model data • Daily multiple (global) model data from Program for Climate Model Diagnosis and Intercomparison (PCMDI) website • Multiple linear regression using both surface and upper air predictors for statistical downscaling • Extreme indices and GEV analysis of model projections • Timeframe: Extreme temperature in 2010 Extreme rainfall in 2011

Comparison between previous and future temperature projections IPCC TAR (2004) IPCC AR4 (2007) Extreme Study (2009/2010) IPCC AR4 Temporal Monthly data Monthly data Daily data Resolution Models 7 16 10 (tentative) Data Volume ~1 G < 10 G ~ 3 T Scenario B1, A1T, B2, A1B, A2 B1, A1B, A2 B1, A1B, A2 and A1FI Predictands Monthly Monthly Daily T max , T mean , T min T mean T max , T mean , T min Monthly Monthly Daily Predictors T max , T mean , T min T mean Surface & upper air (~15 predictors) Downscaling Simple linear Simple linear Multiple stepwise linear regression Method regression regression

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Supplementary Information

A rough estimation of urbanization effect The magnitude of urbanization effect on temperature is taken as the temperature of the urban station (at HKO Headquarters) minus that of a typical rural station (Ta Kwu Ling) of the region. • Annual mean temperature difference between HKO Hq and TKL (T u-r ) for the 18 years data period (1989-2006) is 0.81 ° C. • • In its early establishment, the HKO Hq was in a countryside setting In its early establishment, the HKO Hq was in a countryside setting surrounded by extensive paddy fields (Doberck 1885). Assume the mean value of T u-r for the 18 years data period (1885-1902) is “0”. • The average rate of urbanization between 1885 and 2006 is computed to be 0.08 ° C per decade The future urbanization effect should lie within (i) frozen urbanization (lower bound) (ii) continue at a rate of 0.08 o C per decade (upper bound) (Based on Leung et al . 2007)

Hierarchy of uncertainty in climate projections GHG emission : - future GHG emission is uncertain Large scale GCMs: Large scale GCMs: - projecting future climate is a challenge - skill varies from one model to another Local downscaling: - extracting local climate details from a GCM adds uncertainty

Past and projections of rainfall Hong Kong

Time series of the annual rainfall anomaly (with reference to the 1971-2000 average) in Hong Kong from 1950 to 2008. Bold line represents the 9-year running average.

1200 1000 A2 800 Higher emissions 600 ) 400 scenarios projections 157 mm m m ( y l a 200 m o n have larger variance a l l a f 0 n i a R in the forecast -200 -400 -600 -800 er CO 2 concentration 2000-2009 2010-2019 2020-2029 2030-2039 2040-2049 2050-2059 2060-2069 2070-2079 2080-2089 2090-2099 Decade A1B 1200 1000 800 600 ) m m 400 263 mm ( y l a m o 200 n a l l a f n 0 i a R -200 Higher -400 -400 -600 -800 2000-2009 2010-2019 2020-2029 2030-2039 2040-2049 2050-2059 2060-2069 2070-2079 2080-2089 2090-2099 Decade 1200 B1 1000 800 600 ) m m 325 mm 400 325 mm ( y l a m o 200 n a l l a f n 0 i a R -200 -400 -600 -800 2000-2009 2010-2019 2020-2029 2030-2039 2040-2049 2050-2059 2060-2069 2070-2079 2080-2089 2090-2099 Decade Projected changes in mean annual rainfall in HK under A2, A1B, and B1 scenarios The dark line joining the black dots denotes the multi-model ensemble mean

How good are the computer models ? Simulated annual global mean surface temperature with Hadley Centre model Natural + Anthropogenic (Human-made) Source : Alan J. Thorpe, (2005). Climate Change Prediction : A challenging scientific problem, Institute of Physics